Although we achieve remarkable results on the FF++ dataset, there exist limitations of our framework.
On the one hand, our framework lacks generalization ability on unseen manipulation methods (the results are provided in supplementary material). Our work and Face X-ray [1]} imply that
the discriminative features learned by supervised learning are highly related to manipulation methods.
In our view, that is because forgery evidence is customized for specific manipulation methods due to GAN fingerprints [2]} and some unique operations.
If this explanation holds, not only our approach, but all approaches based on supervised learning will lack generalization ability on unseen manipulation methods.
On the other hand, our framework ignores inter-frame information. Current face manipulation methods generally do not impose constraints on the temporal dimension.
Therefore, the inconsistency between frames should be a valuable cue for video face forgery detection.
